






'''simple docstring'''







import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel


def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			argparse.ArgumentParser()
       parser.add_argument(
           '-m'   ,       '--pretrained_model_name_or_path'   ,       type=a__   ,       default=a__   ,       required=a__   ,       help='Path to pretrained model or model identifier from huggingface.co/models.'   ,       )
       parser.add_argument(
           '-c'   ,       '--caption'   ,       type=a__   ,       default='robotic cat with wings'   ,       help='Text used to generate images.'   ,       )
       parser.add_argument(
           '-n'   ,       '--images_num'   ,       type=a__   ,       default=4   ,       help='How much images to generate.'   ,       )
       parser.add_argument(
           '-s'   ,       '--seed'   ,       type=a__   ,       default=42   ,       help='Seed for random process.'   ,       )
       parser.add_argument(
           '-ci'   ,       '--cuda_id'   ,       type=a__   ,       default=0   ,       help='cuda_id.'   ,       )
       a_						     =			parser.parse_args()
       return args


def       __UpperCamelCase					(      lowercase_       :  Any   ,       lowercase_       :  Tuple   ,       lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       if not len(a__   ) == rows * cols:
              raise ValueError('The specified number of rows and columns are not correct.'   )

       a_     ,	a_						     =			imgs[0].size
       a_						     =			Image.new('RGB'   ,       size=(cols * w, rows * h)   )
       a_     ,	a_						     =			grid.size

       for i, img in enumerate(a__   ):
              grid.paste(a__   ,       box=(i % cols * w, i // cols * h)   )
       return grid


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ,       lowercase_       :  Dict="robotic cat with wings"   ,       lowercase_       :  Dict=7.5   ,       lowercase_       :  Any=50   ,       lowercase_       :  List[str]=1   ,       lowercase_       :  Optional[Any]=42   ,       ):
       """simple docstring"""




       a_						     =			torch.Generator(pipeline.device   ).manual_seed(a__   )
       a_						     =			pipeline(
           a__   ,       guidance_scale=a__   ,       num_inference_steps=a__   ,       generator=a__   ,       num_images_per_prompt=a__   ,       ).images
       a_						     =			int(math.sqrt(a__   )   )
       a_						     =			image_grid(a__   ,       rows=_rows   ,       cols=num_images_per_prompt // _rows   )
       return grid, images


__lowerCAmelCase        =							parse_args()
# Load models and create wrapper for stable diffusion
__lowerCAmelCase        =							CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
__lowerCAmelCase        =							CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
__lowerCAmelCase        =							AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
__lowerCAmelCase        =							UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

__lowerCAmelCase        =							StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__lowerCAmelCase        =							lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
  __lowerCAmelCase        =							load(args.pretrained_model_name_or_path, model=unet)
  unet.eval()
  setattr(pipeline, "unet", unet)
else:
  __lowerCAmelCase        =							unet.to(torch.device("cuda", args.cuda_id))
__lowerCAmelCase        =							pipeline.to(unet.device)
__lowerCAmelCase ,    __lowerCAmelCase        =							generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
__lowerCAmelCase        =							os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
  image.save(os.path.join(dirname, "{}.png".format(idx + 1)))





import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowerCamelCase__  =					get_logger(__name__)




class 					_UpperCAmelCase			(     enum.Enum  ):



 '''simple docstring'''




 __A			       =					'''all_checks'''
 __A			       =					'''basic_checks'''
 __A			       =					'''no_checks'''




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''






def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__=None )      ->int:




  '''simple docstring'''

  if expected_checksums is None:
    logger.info("Unable to verify checksums." )
    return
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(a__ ) - set(a__ ) ) )
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise UnexpectedDownloadedFile(str(set(a__ ) - set(a__ ) ) )
  _UpperCamelCase           =    [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  _UpperCamelCase           =    " for " + verification_name if verification_name is not None else ""
  if len(a__ ) > 0:
    raise NonMatchingChecksumError(
        f'Checksums didn\'t match{for_verification_name}:\n'
        f'{bad_urls}\n'
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
  logger.info("All the checksums matched successfully" + for_verification_name )




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''






def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Dict:




  '''simple docstring'''

  if expected_splits is None:
    logger.info("Unable to verify splits sizes." )
    return
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise ExpectedMoreSplits(str(set(a__ ) - set(a__ ) ) )
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise UnexpectedSplits(str(set(a__ ) - set(a__ ) ) )
  _UpperCamelCase           =    [
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(a__ ) > 0:
    raise NonMatchingSplitsSizesError(str(a__ ) )
  logger.info("All the splits matched successfully." )


def 				lowerCAmelCase__       (			a__    ,     a__ = True )      ->dict:




  '''simple docstring'''

  if record_checksum:
    _UpperCamelCase           =    shaaaa()
    with open(a__    ,     "rb" ) as f:
      for chunk in iter(lambda: f.read(1 << 20 )    ,     B"" ):
        m.update(a__ )
      _UpperCamelCase           =    m.hexdigest()
  else:
    _UpperCamelCase           =    None
  return {"num_bytes": os.path.getsize(a__ ), "checksum": checksum}







def 				lowerCAmelCase__       (			a__ )      ->Any:




  '''simple docstring'''

  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False




